Explainable artificial intelligence modeling to forecast bitcoin prices

John W Goodell, Sami Ben Jabeur (), Foued Saadaoui and Muhammad Ali Nasir
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Sami Ben Jabeur: UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University)

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Abstract: Forecasting cryptocurrency behaviour is an increasingly important issue for investors. However, proposed analytical approaches typically suffer from a lack of explanatory power. In response, we propose for cryptocurrency pricing an explainable artificial intelligence (XAI) framework, including a new feature selection method integrated with a game-theory-based SHapley Additive exPlanations approach and an explainable forecasting framework. This new approach, extendable to other uses, improves both forecasting and model generalizability and interpretability. We demonstrate that XAI modeling is capable of predicting cryptocurrency prices during the recent cryptocurrency downturn identified as associated in part with the Russian-Ukraine war. Modeling reveals the critical inflection points of the daily financial and macroeconomic determinants of the transitions between low and high daily prices. We contribute to financial operating systems research and practice by introducing XAI techniques to enhance the transparency and interpretability of machine learning applications and to support various decision-making processes.

Keywords: Decision support systems; Explainable artificial intelligence; SHAP value; Feature selection; Cryptocurrency prices; Systèmes d'aide à la décision; intelligence artificielle explicable; valeur SHAP; sélection des caractéristiques; prix des crypto-monnaies (search for similar items in EconPapers)
Date: 2023-07
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Published in International Review of Financial Analysis, 2023, 88, pp.102702. ⟨10.1016/j.irfa.2023.102702⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05148944

DOI: 10.1016/j.irfa.2023.102702

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